Enter the Age of the AI-Driven Enterprise: The 5 Pillars of Data Modernization

Data modernization is a key business goal for those seeking the maximum benefit from the AI-powered cloud. Without it, legacy data remains inaccessible, and silos can hamper productivity efforts and damage any concept of a company becoming a cloud-native business.

Within any business, there are some leaders and system operators who see data in any form as “good” and/or view artificial intelligence as a plug-in hero that can drive their business forward. Both types are in for a shock should they ever attempt to lead an AI-based initiative without undergoing data modernization first.

Many organizations build up data through their early days in an ad hoc fashion to meet fast-changing business needs, when the focus on sales and results is everything. Only when they mature and realize a mix of disparate data streams need to work together does any data organization take place.

When it comes to adopting the cloud and AI services, proper data integration and governance is a key prerequisite if the business wants to see any value from their investment and effort. Identify your data and see if it fits in these pillars and processes to ensure any AI effort will deliver strong results:

The future belongs to the data-driven

Moving from what's now to what's next starts with modernizing the digital core of your business.

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The 5 pillars of data modernization

1. Identify your strategic data

The key question when starting any digital project, but especially a data/AI initiative, is how does your data and AI results align with your business strategy, and how will it help meet those goals/objectives? Sales, supply chain and production data are usually the golden ticket items for AI. When it comes to real-world AI and business intelligence success, using that data, be it IoT, customer, or prospect information aligned to the business strategy is the key to creating the first pillar.

2. Follow the business processes, not the data

Businesses have all sorts of data, and the belief that it can be simply added into an AI that will produce profit-boosting answers is not accurate. Instead, digging into the strategy from the previous point, focus on your key business processes, and identify the data that relates to them. Ensure those data streams match business goals with key KPIs and are portable in order to drive your AI ambitions.

3. Data management and governance

Data should be findable, accessible, interoperable, reusable (FAIR, according to EU data principles). If it isn’t, that data’s value decreases to worthless very fast. Having identified the data from pillars 1 and 2, ensure that the data is accurate and of high quality before testing it on any AI system.

4. Build the right solution for your business

So many vendors offer AI solutions, either as part of a broader cloud or enterprise package, or for a specialist vertical or niche. Ensure that whatever you select fits your business, delivers the results that will drive and meet those objectives, and will play a key role in your company’s future. Many organizations have highly focused AI products that may be great in one area, but will complicate wider business-strategy-focused efforts.

That solution should fit into your overall architecture or as part of your growing cloud solution, and feature the APIs or plug-ins required to work with all your data. Alternatively, you should have a data conversion expert on standby to shoehorn unwieldy sources into place.

5. Remember the data rules

Data is already covered by a myriad of regulations from HIPAA to GDPR and many more in regulated industries. Ensure that your data follows those rules for security and privacy and meets all of your industry regulations and best practices.

Artificial intelligence is also coming under legal scrutiny, with a warning glare being directed at black box AIs that may deliver unexplainable results. The EU’s draft AI regulations are worth reviewing to get an idea of any future regulations or concerns, such as AIs that create unacceptable risks around distorting human behavior or decisions (which could apply to business decisions).

The human aspects of data and AI

By building the AI across these pillars, any company should get strong and positive results from their AI analytics or advisory service. They are already helping many companies with predictive maintenance, supply chain, production, and sales initiatives in unearthing valuable insights.

Yet many companies will likely adopt AI as part of a cloud service and feed in their data, regardless of the pillars above. Before you get to this point, you should decide whether your business has the in-house knowledge, or access to expertise, to help ensure the right people are driving decisions around creating systems that generate, manage, and use data between business units.

The growing number of AI strategist and data scientist roles in enterprises highlights how seriously their efforts are managed. But even smaller organizations need to understand a little of the science and numbers behind the AI-magic to make sure they have realistic expectations for a project, and how those results can impact business decision making.

People should be in control of decisions and aware of their consequences across all those pillars, especially as AI appears in more cloud and digital services, or is used by partner businesses in their digital operations that impact yours.

Whatever your opinions from across the boardroom or IT team, AI is not going away and will be a key part of all decision-making processes in the coming years. Governments, businesses, and consumer services are or will use it, and there is only a short window to gain a key competitive advantage from using AI to make smarter decisions.